Example: Given class is spam and the classifier has been correctly predicted it as spam.Example: Given class is spam however, the classifier has been incorrectly predicted it as non-spam.Example: Given class is spam however, the classifier has been incorrectly predicted it as non-spam.Example: Given class is spam however, the classifier has been incorrectly predicted it as non-spam.Question: In the medical example, what is worse, a False Positive, or a False Negative?
Answer: A False Positive implies sending a healthy person to get more tests. This is slightly inconvenient, but ok. A False Negative implies sending a sick person home, which can be disastrous!
Question: In the email example, what is worse, a False Positive, or a False Negative?
Answer: A False Negative implies a spam message will make its way into your inbox. This is slightly inconvenient, but ok. A False Positive implies missing an e-mail from your dear grandma, which can be disastrous!
-F1 score is a weighted average of the recall (sensitivity) and precision. F1 score might be good choice when you seek to balance between Precision and Recall.
Conclusion: